Steganography is one of the ways to hide data between parties. Its use can be worrisome, e.g., to hide illegal communications. Researchers found that public blockchains can be an attractive place to hide communications; however, there is not much evidence of actual use in blockchains. Besides, previous work showed a lack of steganalysis methods for blockchains. In this context, we present a steganalysis approach for blockchains, evaluating it in Bitcoin and Ethereum, both popular cryptocurrencies. The main objective is to answer if one can find steganography in real case scenarios, focusing on LSB of addresses and nonces. Our sequential analysis included 253 GiB and 107 GiB of bitcoin and ethereum, respectively. We also analyzed up to 98 million bitcoin clusters. We found that bitcoin clusters could carry up to 360 KiB of hidden data if used for such a purpose. We have not found any concrete evidence of hidden data in the blockchains. The sequential analysis may not capture the perspective of the users of the blockchain network. In this case, we recommend clustering analysis, but it depends on the clustering method’s accuracy. Steganalysis is an essential aspect of blockchain security.

Abstract:The validation of transformations in the Model-Driven Engineering (MDE) context is important to ensure the quality and correctness of the models. The validation of MDE transformation is burdensome due to both the complexity of the models and the variety of languages that implement them. A test case generation technique can be applied to support the validation process; however, it is challenging because of the size of the test cases set. This paper presents a case study which applies test case generation based on the SysML metamodel for MDE. A development approach for embedded systems, named SyMPLES, was applied. This approach transforms SysML models to Simulink. Two policies were evaluated in the test case generation based on the SysML metamodel. Moreover, a set of strategies and coverage criteria were applied in order to reduce the set of test cases generated and evaluate its effectiveness. The results showed that relevant errors were identified in the model transformation. The use of generation policies also improve the effectiveness of the test case set generated.

No abstract